计算机科学
回归
过程(计算)
开普勒
决策树
分子
生物系统
数据挖掘
算法
材料科学
化学
数学
生物
统计
有机化学
星星
操作系统
计算机视觉
作者
Rajesh Kondabala,Vijay Kumar,Amjad Ali,Manjit Kaur
标识
DOI:10.1142/s0217984920503467
摘要
In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.
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